The relationships between crop productivity and climate variability drivers are often assumed to be stationary over time. However, this may not be true in a warming climate. Here we use a crop model and a machine learning algorithm to demonstrate the changing impacts of climate drivers on wheat productivity in Australia. We find that, from the end of the nineteenth century to the 1980s, wheat productivity was mainly subject to the impacts of the El Niño Southern Oscillation. Since the 1990s, the impacts from the El Niño Southern Oscillation have been decreasing, but those from the Indian Ocean Dipole have been increasing. The warming climate has brought more occurrences of positive Indian Ocean Dipole events, resulting in severe yield reductions in recent decades. Our findings highlight the need to adapt seasonal forecasting to the changing impacts of climate variability to inform the management of climate-induced yield losses.

作物生产力和气候变率驱动因素之间的关系通常被认为随着时间的推移是固定的。然而，这在气候变暖的情况下可能并非如此。在这里，我们使用作物模型和机器学习算法来展示气候驱动因素对澳大利亚小麦生产力不断变化的影响。我们发现，从19世纪末到80年代，小麦产量主要受厄尔尼诺南方涛动的影响。1990年代以来，厄尔尼诺南方涛动的影响一直在减弱，而印度洋偶极子的影响却在增加。气候变暖带来了更多的印度洋正偶极子事件发生，导致近几十年来产量严重下降。